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The Transformer architecture is a deep learning model introduced in the paper “Attention is All You Need” by Vaswani et al. in 2017. It revolutionized various natural language processing (NLP) tasks and has since been applied to many other domains.
Here are key components of the Transformer architecture:
Self-Attention Mechanism: The core innovation of Transformers is the self-attention mechanism, which allows the model to weigh the importance of different input tokens when making predictions. It considers relationships between all tokens simultaneously, unlike previous models that relied on fixed-size context windows.
Transformers: On the Power of ‘Attention’
In this article, we explore the mechanics of attention, encoder-decoder dynamics, and positional encodings, unraveling…
Multi-Head Attention: Transformers use multiple attention heads, each focusing on different parts of the input. This enables the model to learn different aspects of relationships between words, providing richer representations.
Positional Encoding: Since Transformers lack a built-in sense of position or order in sequences, positional encoding is added to the input embeddings to provide the model with information about token positions.
Stacked Layers: Transformers consist of multiple layers, typically encoding and decoding layers. Each layer contains a multi-head self-attention mechanism and feedforward neural networks. Stacking these layers enables the model to capture hierarchical features and representations.
Residual Connections: Residual connections, or skip connections, are used to help gradients flow effectively through the model during training. They connect the output of one layer to the input of a subsequent layer.
Layer Normalization: Layer normalization is applied before each sub-layer in the Transformer to stabilize training.